Problem
Real estate firms spent hours every two weeks manually searching StreetEasy, copying listing data into spreadsheets, and formatting it for analysis. By the time the research was compiled, market conditions had shifted and the process started again, creating a recurring time sink.
Solution
A Python pipeline orchestrated through n8n that scraped StreetEasy listings on a 2-week cadence. It extracted key data (price, location, square footage, days on market), normalized it, and pushed the structured results directly into the client's existing Google Sheet. The client opened the same spreadsheet they'd always used; it was automatically populated with current market data.
Results
- Recurring research ran on its own every two weeks
- Delivered structured data on a fixed cadence
- Delivered into client's existing workflow
Built a Python scraping pipeline orchestrated through n8n that ran on a 2-week schedule. The scraper extracted key listing data (price, location, sqft, days on market), normalized it, and pushed structured results directly into a shared Google Sheet via the Sheets API. It included error handling and retry logic. BeautifulSoup handled HTML parsing; n8n managed scheduling, error alerting, and the final Sheets write.
Tech Stack
- Python
- n8n
- Google Sheets API
- BeautifulSoup